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Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study

Macdonald, Trystan; Dinnes, Jacqueline; Maniatopoulos, Gregory; Taylor-Phillips, Sian; Shinkins, Bethany; Hogg, Jeffry; Dunbar, John Kevin; Solebo, Ameenat Lola; Sutton, Hannah; Attwood, John; Pogose, Michael; Given-Wilson, Rosalind; Greaves, Felix; Macrae, Carl; Pearson, Russell; Bamford, Daniel; Tufail, Adnan; Liu, Xiaoxuan; Denniston, Alastair K

Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study Thumbnail


Authors

Trystan Macdonald

Jacqueline Dinnes

Gregory Maniatopoulos

Sian Taylor-Phillips

Bethany Shinkins

Jeffry Hogg

John Kevin Dunbar

Ameenat Lola Solebo

Hannah Sutton

John Attwood

Michael Pogose

Rosalind Given-Wilson

Felix Greaves

Russell Pearson

Daniel Bamford

Adnan Tufail

Xiaoxuan Liu

Alastair K Denniston



Abstract

Background: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. Objective: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. Methods: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence’s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from “definitely exclude” to “definitely include,” and suggest edits. The document will be iterated between rounds based on participants’ feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. Results: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. Conclusions: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas.

Citation

Macdonald, T., Dinnes, J., Maniatopoulos, G., Taylor-Phillips, S., Shinkins, B., Hogg, J., Dunbar, J. K., Solebo, A. L., Sutton, H., Attwood, J., Pogose, M., Given-Wilson, R., Greaves, F., Macrae, C., Pearson, R., Bamford, D., Tufail, A., Liu, X., & Denniston, A. K. (2024). Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study. JMIR Research Protocols, 13(1), Article e50568. https://doi.org/10.2196/50568

Journal Article Type Article
Acceptance Date Feb 13, 2024
Online Publication Date Mar 27, 2024
Publication Date 2024
Deposit Date Apr 11, 2024
Publicly Available Date Apr 16, 2024
Journal JMIR Research Protocols
Electronic ISSN 1929-0748
Publisher JMIR Publications
Peer Reviewed Peer Reviewed
Volume 13
Issue 1
Article Number e50568
DOI https://doi.org/10.2196/50568
Keywords Artificial intelligence ; design ; developers ; diabetes mellitus ; diabetic eye screening ; diabetic retinopathy ; diabetic ; DM ; England ; eye screening ; imaging analysis software ; implementation ; machine learning ; retinal imaging ; study protocol
Public URL https://nottingham-repository.worktribe.com/output/33563369
Publisher URL https://www.researchprotocols.org/2024/1/e50568